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Developmental Cognitive Neuroscience

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Socioeconomic status and neural processing of a go/no-go task inpreschoolers: An assessment of the P3bAshley M. St. John⁎, Kayla Finch, Amanda R. TarulloDepartment of Psychological and Brain Sciences, Boston University, 64 Cummington Mall, Boston, MA, 02215, United States

A R T I C L E I N F O

Keywords:Socioeconomic statusIncomeExecutive functionP3bERPPreschool

A B S T R A C T

While it is well established that lower socioeconomic status (SES) is associated with poorer executive functioning(EF), how SES relates to the neural processing of EF in childhood remains largely unexplored. We examined howhousehold income and parent education related to amplitudes of the P3b, an event-related potential component,during one EF task. We assessed the P3b, indexing inhibition and attention allocation processes, given theimportance of these skills for academic success. Children aged 4.5–5.5 years completed a go/no-task, whichassesses inhibitory control and attention, while recording EEG. The P3b was assessed for both go trials (indexingsustained attention) and no-go trials (indexing inhibition processes). Higher household income was related tolarger P3b amplitudes on both go and no-go trials. This was a highly educated sample, thus results indicate thatP3b amplitudes are sensitive to household income even within the context of high parental education. Findingsbuild on the behavioral literature and demonstrate that SES also has implications for the neural mechanismsunderlying inhibition and attention processing in early childhood.

1. Introduction

Socioeconomic status (SES) has broad implications for development,with high SES parents able to invest time and money in their children’sdevelopment and children from lower SES families at risk for adverseoutcomes including poorer health, psychological well-being, and aca-demic achievement (see for review; Bradley and Corwyn, 2002;Hackman et al., 2010; Reardon, 2011). In particular, the SES gap inexecutive functioning (EF), encompassing higher-order cognitivethinking, is evident by kindergarten (Farah et al., 2006; Raver et al.,2013). Despite implications of EF for long-term academic and socio-emotional outcomes (de Wilde et al., 2015; Morgan et al., 2018; Riggset al., 2006), the mechanisms underlying SES-EF associations are notfully understood.One promising approach is to assess how SES relates to neural

mechanisms of EF at school entry, when EF is rapidly developing(Carlson, 2005; Farah, 2017). Given that many processes underlie be-havioral performance, neural measures help tease apart specific aspectsof processing while children perform EF tasks. Indeed, neural proces-sing has been proposed to be a factor underlying socioeconomic gaps incognitive development (Pavlakis et al., 2015). Thus, the brain could bethe intermediary in explaining how SES shapes life outcomes (Farah,2017) and could serve as an underlying mechanism in understanding

how SES shapes EF. Additionally, the practical implication of assessingneural processes is their potential to serve as biomarkers with pre-dictive power for later outcomes (Farah, 2017; Gabrieli et al., 2015;Raizada and Kishiyama, 2010). EF demands increase throughout schooland EF becomes increasingly essential for academic success as childrenget older (McClelland and Cameron, 2012). Therefore, neural measurescould aid in predicting EF and academic performance (Greenberg,2006; Harms et al., 2014; Raizada and Kishiyama, 2010).

1.1. Event-related potentials and the P3b

Event-related potentials (ERPs) are a feasible method for under-standing how children neurally process EF tasks (Grammer et al., 2014;Willner et al., 2015). One widely studied ERP component is the P3b,indexing inhibition processes and sustained attention (Davis et al.,2003; Eimer, 1993). It is the third positive peak in the ERP waveformand is typically assessed in parietal regions (see for review, Polich,2007). The P3b has been assessed in children during flanker (Ruedaet al., 2004) and go/no-go tasks (Willner et al., 2015). The go/no-gotaps multifaceted inhibitory control processes, as children must controltheir attention and focus on the task but also must inhibit motor re-sponses to the target stimulus.The P3b is a probable candidate that may vary by SES. In one

https://doi.org/10.1016/j.dcn.2019.100677Received 17 October 2018; Received in revised form 4 June 2019; Accepted 19 June 2019

⁎ Corresponding author at: Department of Psychological and Brain Sciences, Boston University, 64 Cummington Mall, Boston, MA, 02215, United States.E-mail addresses: astjohn@bu.edu (A.M. St. John), kfinch@bu.edu (K. Finch), Atarullo@bu.edu (A.R. Tarullo).

Developmental Cognitive Neuroscience 38 (2019) 100677

Available online 22 June 20191878-9293/ © 2019 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

T

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kindergarten study, P3b amplitudes were reported for the go conditiononly during a go/no-go where children won and lost points; and largerP3b amplitudes predicted better academic performance in first grade(Willner et al., 2015). This demonstrates the practical role of ERPs inpredicting outcomes. Further, given that inhibition and attention varyby SES (e.g., Lawson et al., 2017) and these processes are critical forlearning and academic success (Allan et al., 2014; Diamond, 2013), anERP component that indexes these processes is a probable candidate tovary by SES. Moreover, research suggests the P3b increases in ampli-tude through adolescence (Downes et al., 2017). Taken together, it ispossible that higher SES would relate to larger P3b amplitudes, perhapsindicating more developed neural processing.

1.2. Income, parent education, and neural processing

When assessing socioeconomic context, it is important to considerhow to conceptualize and operationalize SES. SES is a complex con-struct that reflects financial resources and capital (Hackman and Farah,2009). The most common indicators are household income, parentaleducation, and parental occupation (see for review, Ursache and Noble,2016). There is debate whether to combine these measures into acomposite or assess them separately (Ursache and Noble, 2016). Whileusing composite SES measures is common, others argue that SES con-structs (e.g., income and parent education) have different implicationsfor development and are conceptually distinct (Duncan and Magnuson,2003, 2012). For instance, household income has been more related toacademic success while parent education has been associated with bothacademic and behavioral outcomes (Duncan and Magnuson, 2003).Additionally, these aspects of SES differentially relate to structural

brain development as income was associated with cortical thicknesswhile parent education related to hippocampal volume (Noble et al.,2015). Moreover, other studies have assessed how only one aspect ofSES (such as parent education) relates to the brain in childhood(Stevens et al., 2009). It is possible that income and parent educationcould relate to brain development and adaptive outcomes via differentmechanisms. Parent education may be more related to parenting stylewhile income could enable access to learning materials and higherquality child-care. Therefore, assessing how income and parent edu-cation separately relate to neural EF processes would provide a morecomprehensive and specific assessment of the role of the socioeconomiccontext.

1.3. Current study

Given that low SES children are at risk for poor EF by kindergartenentry, it is critical to understand the neural mechanisms that contributeto this disparity. The goal of this study was to take a first step andexamine how indices of SES relate to the P3b in a go/no-go task in4.5–5.5 year olds. This task taps two aspects of EF including inhibitionand attention processes. Based on previous literature, we expected theP3b to be larger on no-go trials compared to go trials (Abdul Rahmanet al., 2017; Davis et al., 2003). We explored the separate contributionsof household income and parent education to provide specificity in howsocioeconomic context may relate to neural processing. We assessed theP3b on go and no-go trials to examine whether income and parenteducation mattered for the P3b on sustained attention (go) or inhibition(no-go) trials, or whether there were global effects. We expected higherincome and higher levels of parent education would be associated withlarger P3b amplitudes.

2. Methods

2.1. Participants

Participants were 69 children (40 females) aged 4.5–5.5 years andtheir primary caregivers. Children spoke and understood English.

Children were full-term singletons with no known hearing, visual,neurological, or developmental disorders. The primary goal of thisstudy was to assess how income and parent education related to the P3bon go and no-go trials. Therefore to be included in analyses, (1) childrenhad to have useable ERP data for both trial types and (2) childrenneeded to understand the task. This ensured that ERPs included inanalyses were only from children engaging in the task. Therefore while125 children participated in the study, 56 were excluded leaving a finalsample of 69. Children were excluded for the following reasons: did notunderstand the task (N=3), which was defined as go accuracy was lessthan 70% and no-go accuracy was higher than their go accuracy; de-clined to complete the task (N=6); did not have usable ERPs for bothtrial types (go and no-go; N=29); had EEG technical difficulties(N= 5); had braided hair, which prevented electrodes from sufficientlycontacting the scalp (N=4); and declined to wear the EEG cap (N=9).There were no differences in child age, child gender, income, or parenteducation, between the group of children included in the final sampleand those excluded.Of the final sample, 26 children attended preschool, 15 children

were in kindergarten, and 28 children did not attend preschool orkindergarten. See Table 1 for demographic information of the finalsample.Effort was made to recruit a sample across the SES spectrum. Thus

38.20% of the sample was at or below an income-to-needs (ITN) ratio of3.0, meaning they had an income less than three times the federalpoverty line, given their household size. Given the high cost of living inthe city where this study was conducted, an income below the thresholdof 3 times the federal poverty line is considered financially strained(Ames, Lowe, Dowd, Liberman, & Youngblood, 2013). See Table 2 for abreakdown of the income and parent education distribution in thesample.

2.2. General procedure

Participants were recruited from a department-maintained databaseof families interested in research; from publicly available state birthrecords; from online advertising; and through face-to-face-recruitmentevents at Head Starts, diaper banks, community play groups, and acommunity health center. This study was approved by the universityInstitutional Review Board. Parent-child dyads visited the laboratoryfor one session lasting 1.5–2.0 h. Following informed consent, the ex-perimenter presented the child with a sticker card with the child’s nameon it and explained that by working hard at the game, they could earnstickers. Next the experimenter placed the EEG cap on the child’s head.While EEG was recording, the child completed a computerized go/no-go task in an electrically shielded booth to prevent interference with theEEG signal. The task was administered via E-Prime 2.0 software(Psychology Software Tools, Pittsburgh, PA, USA). Next the child

Table 1Demographics.

Maternal age (years) Minimum Maximum

M (SD) 36.24 (5.76) 22.00 48.00Paternal age (years)M (SD) 39.09 (7.50) 23.00 56.00

Child age (years)M (SD) 5.04 (0.27) 4.55 5.52

Child ethnicityWhite 46.40 %Black 7.20 %Hispanic/Latino 7.20 %Asian 14.50 %Multiracial 24.60 %

Income-to-needs ratio; M (SD) 4.88 (3.72) 0.42 18.42Parent Education Average; M (SD) 4.03 (1.03) 1.50 5.00

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completed assessments of receptive language and nonverbal IQ. Theparent completed a demographics questionnaire.

2.3. Measures

2.3.1. Go/No-goChildren were told that all of the animals had escaped from their

cages at the zoo and the zookeeper needed their help to catch them (Heet al., 2010; Lamm et al., 2014), but that the friendly orangutans arehelping them catch the animals. Therefore children were told to press abutton to catch the animals each time they saw an animal (go trial) butnot to press the button when they saw an orangutan (no-go trial). Thuschildren had to inhibit their dominant responses on no-go trials. Chil-dren completed 18 practice trials and the rules were repeated halfwaythrough the task. Each trial consisted of an animal stimulus presentedfor 750ms and then a blank screen presented for 500ms. Each trial wasfollowed by a blank screen with a randomized inter-trial interval be-tween 200–300ms. Children could respond during the presentation ofthe stimulus or on the blank screen. Therefore, the trial ended either atthe end of the 500ms blank screen or when the child pushed the button,whichever came first (Fig. 1).Children completed a total of 280 trials of which 75% were go trials

and 25% were no-trials. The trials were broken up into four blocks.Children were shown a map of the zoo at the beginning of the task andafter each block so they could track their progress and they receivedtwo stickers at the end of each block. Accuracy was computed for eachtrial type (go and no-go) and block. The go trials index sustained orselective attention while the no-go trials index actual inhibition pro-cesses (Lewis et al., 2017; Willner et al., 2015). Reaction time wascalculated as the mean reaction time on correct go trials only. Trialswith reaction times< 150ms were excluded prior to computing themean reaction time. The task took around 12min to complete. SeeTable 3 for task descriptive statistics.

2.4. Household income

Parents reported on household income and household composition.An ITN ratio based on the federal poverty level was computed withincome and household composition.

2.4.1. Parent educationThe parent reported highest maternal and paternal educational

level. They were coded from 1 (some middle school or high school) to 5(graduate degree). Scores were averaged to yield a parent educationcomposite.

2.4.2. Tested covariates2.4.2.1. Language. Children completed the picture vocabulary test(normed for ages 3–85) from the National Institutes of HealthToolbox Cognition Battery (Gershon et al., 2014). This measuresreceptive vocabulary and uses a computerized adaptive format basedon performance. The child hears a word and sees four photographs onthe screen and is asked to select the picture that most closely matchesthe meaning of the word. Age-adjusted scores were used. Two childrendid not have scores due to technical difficulties.

2.4.2.2. Nonverbal IQ. The matrices sub-scale of the Kaufman BriefIntelligence Test, Second Edition, was used. The assessment is multiplechoice and involves the child pointing to pictures that reflect anunderstanding of both meaningful and abstract relationships. The tasktakes 5–10min to complete. Age-adjusted scores were used. Thisassessment was not administered to two children.

2.5. Electrophysiological recording and analysis

EEG was recorded to a vertex reference using NetStation acquisitionsoftware and a Net Amp 300 amplifier (Electrical Geodesics, Inc.:

Table 2ITN and Parent Education Information.

ITN grouping Frequency Percent Cumulative Percent

0.00–1.00 5 7.40 % 7.40 %1.00–2.00 10 14.70 % 22.10 %2.00–3.00 11 15.90 % 38.20 %3.00–4.00 9 13.20 % 51.50 %4.00–5.00 5 7.40 % 58.80 %5.00–6.00 7 10.30 % 69.10 %6.00–7.00 6 8.80 % 77.90 %7.00–8.00 4 5.90 % 83.80 %8.00–9.00 4 5.90 % 89.70 %9.00–10.00 1 1.50 % 91.20 %>10.00 6 8.80 % 100 %

Maternal EducationSome middle school or some highschool

3 4.30 % 4.30 %

High school graduate or GED 3 4.30 % 8.70 %Some college 8 11.60 % 20.30 %4-year college degree 20 29.00 % 49.30 %Graduate degree 35 50.70 % 100.00 %

Paternal EducationSome middle school or some highschool

2 2.90 % 2.90 %

High school graduate or GED 12 17.60 % 20.60 %Some college 8 11.80 % 32.40 %4-year college degree 14 20.60 % 52.90 %Graduate degree 32 47.10 % 100.00 %

Note: ITN refers to the level of a household’s income relative to the federalpoverty line. Thus an ITN of 1.00 means that a family has an income at thefederal poverty line. An ITN of 2.00 means that family has an income 2x that ofthe federal poverty line, etc.

Fig. 1. Visual depiction of the go/no-go task. Children could respond during thestimulus presentation or during the 500ms blank screen.

Table 3Task Descriptive Statistics.

M (SD) Min Max N

Mean P3b amplitude for go trials 16.68 (9.47) −4.17 43.04 69Mean P3b amplitude for no-go trials 19.83 (11.86) −6.71 47.52 69Accuracy in go trials .92 (.06) .74 1.00 69Accuracy in no-go trials .71 (.17) .33 .99 69Reaction time in accurate go trials (ms) 658.87 (66.60) 504.18 816.84 69Language 105.91 (13.33) 78.75 135.37 67Nonverbal IQ 102.00 (10.97) 83.00 141.00 67

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Eugene, OR) connected to a Geodesic Sensor Net with 128 electrodesspaced ∼1 cm apart over the scalp. Prior to use, the 128 lead high-density net was soaked for 10min in an electrolyte solution (6cc po-tassium chloride/liter distilled water) to facilitate electrical contactbetween the scalp and electrodes. Prior to recording, impedances werelowered by administering small amounts of the electrolyte solution toelectrodes with poor contact. Data were sampled from all channels at500 Hz.Offline data was processed using NetStation. A bandpass filter of

.3–30 Hz was applied. Continuous EEG data was then was segmentedtime-locked to trial onset from -100 prior to the trial to 1000ms afterthe trial. As with the behavioral data, only trials in which the childresponded correctly were included and additionally for go trials, whenreaction time was> 150ms. Each segment was baseline corrected,using the mean voltage in the 100ms prior to stimulus onset. Next, anautomatic artifact rejection paradigm identified channels with ex-cessive artifact (> 150 μV). In addition, segments with eye blinks(> 140 μV differential average) or eye movements (> 100 μV differ-ential average) were excluded. Next bad channels were replaced viainterpolation and segments for each child were averaged within eachtrial type (go and no-go) and re-referenced to the average reference.Each child had to have at least 10 useable trials for each trial type to beincluded in analyses. For the final sample, this resulted in an average63.45 (SD=32.45) go trials and 24.59 (SD=12.11) no-go trials.Mean P3b amplitude was computed in the time window 400–700ms

post-stimulus in the parietal region for go trials and no-go trials. Thistime window was selected to be generally consistent with past research(McDermott et al., 2012; Willner et al., 2015). Visual inspection of thegrand-averaged waveforms confirmed that the P3b occurred in thewindow from 400 to 700ms post-stimulus. Further, individual wave-forms were inspected and time windows were adjusted if needed toensure that the P3b was represented. This was the case for four chil-dren. For one child, the revised time window was 300–600ms and forthree other children, the time windows were adjusted to 350–650ms.Visual inspection indicated that the P3b was maximal in the parietalregion, consistent with past research (Davis et al., 2003; McDermottet al., 2012; Willner et al., 2015). The following electrodes with theircorresponding 10–20 system sites were averaged into one parietal re-gion: 61 (P1), 62 (Pz), 67 (PO3), 72 (POZ), 77 (PO4), 78 (P2; see Fig. 2).The mean amplitude of the go P3b was not correlated with the numberof useable go segments, r (67)= .008, p= .95, and the mean amplitudeof the no-go P3b was not correlated with the number of useable no-gosegments, r (67) = −.06, p = .65.

2.6. Analysis plan

A preliminary model was first run to check that the paradigm eli-cited the expected differences in P3 amplitude. We expected larger P3bamplitudes on no-go trials compared to go trials. A repeated measuresanalysis of variance (RM-ANOVA) was used with trial type as a within-subjects factor. Next, Pearson correlations were used to assess relationsof behavioral performance to the P3b. In addition, Pearson correlationswere used to assess relations of ITN and parent education to behavioralperformance.To examine possible associations of child gender, age, language, and

nonverbal IQ to the P3b, each covariate was included in the repeatedmeasures model. Gender was included as a between-subjects factor andcontinuous variables were included as covariates. Whenever there wasan effect of the covariate in the model, the covariate was included insubsequent models. In addition, the relations between child age withincome and parent education were tested using Pearson correlations.To assess relations of ITN and parent education to the P3b, we used

repeated measures analyses of covariance (RM-ANCOVAs) with the P3bas the dependent measure. Trial type (go or no-go) was included as awithin-subjects factor. ITN and parent education were included aspredictors in separate models. This allowed us to test for main effects of

ITN and parent education as well as interactions. Statistically all con-tinuous variables were entered as covariates (Hoffman, 2015; Sweetand Grace-Martin, 2011). We use the term “predictor” for ITN andparent education to distinguish from variables that we treated as po-tential covariates (e.g., age).For all ANOVAs, post hoc analyses followed significant main effects,

using Bonferroni corrections for multiple testing. In ANOVAs where theassumption of sphericity was violated, we used Greenhouse–Geissercorrections.

3. Results

3.1. Preliminary analyses

We first used a RM-ANOVA to test for an effect of trial type. Aswould be expected, there was a main effect of trial type, F (1,68)= 18.10, p< .001, ηp2= .21, such that P3b amplitudes were largeron no-go trials (M=19.83 μV, SD=11.86) compared to go trials(M=16.68 μV, SD=9.47). See Fig. 3a.Accuracy on go trials was significantly correlated with the go P3b, r

(67)= .27, p= .03, such that higher accuracy related to larger go P3bamplitudes. No other correlations associating behavioral performanceand P3b amplitudes were significant. See Table 4 for correlations ofstudy variables. In regards to behavioral performance and SES indices,ITN and parent education did not relate to accuracy or reaction time.Age, language, and nonverbal IQ did not relate to P3b amplitude

when included in the RM-ANCOVA, with trial type (go and no-go) as awithin-subjects factor. When gender was included, there as a trial type xgender interaction, F (1, 67)= 6.31, p = .014, ηp2= .09. While theparameter estimates did not show a significant gender difference oneither trial type, the pattern suggested that females had larger P3bamplitudes on go trials (M=17.91 μV, SD=9.38) compared to males(M=14.99 μV, SD=9.51). There were no differences in no-go P3bamplitude in females (M=19.53 μV, SD=12.02) compared to males(M=20.23 μV, SD=11.84). Gender was thus included in subsequentmodels. Finally, child age was not significantly correlated with ITN orparent education (see Table 4).

3.2. Relations of ITN and parent education to P3b amplitudes

We ran two RM-ANCOVAs to test the roles of ITN and parent edu-cation. In the first model, trial type (go or no-go) was a within-subjectsfactor, gender was a between-subjects factor, and ITN was a predictor.There was a main effect of ITN, F (1, 65)= 4.21, p = .04, ηp2= .06.Assessment of the parameter estimates indicated that higher ITN wasassociated with larger P3b amplitudes (see Fig. 3b and c). There was notrial type x ITN interaction.In the next model to test for effects of parent education, we used a

RM-ANCOVA with trial type (go or no-go) as a within-subjects factor,gender as a between-subjects factor, and parent education as a pre-dictor. There was no main effect of parent education or interactionswith parent education.

4. Discussion

We examined how indices of SES related to electrophysiologicalprocessing of a go/no-go task in early childhood. We focused on theP3b, an index of inhibition and attention allocation processes, given therelevance of this ERP component as a predictor of later academic out-comes. Children aged 4.5–5.5 years completed a go/no-go task and P3bamplitudes were calculated for go and no-go trials. Thus we focused ontwo aspects of EF that were assessed in the task: inhibitory control andattention processes. Even though the sample was highly educated, re-sults showed that higher household income was associated with largerP3b amplitudes of both go and no-go trials. Given the SES disparities inEF and ultimately academic achievement, results highlight the potential

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relevance of neural processing as a mechanism to understand thesebehavioral differences.To our knowledge, this is the first study to demonstrate that the P3b

in early childhood varies by socioeconomic context, specificallyhousehold income. Studies have begun to assess the neural bases of EFin early childhood (e.g., Downes et al., 2017) and the association be-tween SES and behavioral measures of EF is well-established (e.g.,Lawson et al., 2017). Yet to our knowledge this is one the first studies toexamine how SES also matters for neural processing of some aspects ofEF during a go/no-go task. We focused on the P3b as it indexes complexattention processes and inhibition (Polich, 2007), all relevant aspects ofEF that are critical for academic success (Allan et al., 2014). Resultsindicate the P3b is sensitive to the environment, generally consistentwith one study finding that P3b amplitudes were larger on no-go trialsfor never institutionalized children compared to a foster care group(McDermott et al., 2012). However, P3b amplitudes did not differ on gotrials and the nature of this sample should be acknowledged, such thatthe foster care group was comprised of children who were originallyraised in institutional care and thus experienced extreme early psy-chosocial deprivation. In addition, this result builds on the small bodyof literature that has found SES differences on ERPs of auditory selec-tive attention (D’Angiulli et al., 2008; Stevens et al., 2009) and visualtarget detection (Kishiyama et al., 2008). Our finding demonstrates thatSES also is important for neural attention and inhibition processes thatare required to perform go/no-go task.Past research speaks to the predictive power of the P3b such that

higher P3b amplitudes in kindergarten predicted better adaptivelearning behaviors in first grade (Willner et al., 2015). Concurrent re-lations have also been found between the P3b and academic achieve-ment (Harmon-Jones et al., 1997; Hillman et al., 2012), suggesting theimportance of neural inhibition and attention processing for schoolsuccess. We extend this literature by assessing how the socioeconomiccontext may play a role in the development of the P3b, as we show SES

linked differences in the P3b by school entry. Future research is neededto longitudinally assess these constructs throughout early childhood tomore comprehensively understand how the P3b may mediate relationsbetween SES and later EF and academic outcomes.An important question is why we see differences in P3b amplitude

by household income. This is notable, given that this sample was highlyeducated. Thus, even within this highly educated sample, incomemattered. SES co-occurs with other risks and income could thus reflectdifferent aspects of the child’s environment. Families with lower in-comes may only be able to afford to live in areas with more environ-mental risks and toxin exposure (see for review, Evans, 2004; Hackmanand Farah, 2009) which could negatively affect brain development.Chronic stress also is a probable mechanism (Hackman and Farah,2009). Living on a low income could be stressful for many reasons in-cluding worry about affording rent or enough food. Indeed, lower in-come families have more food insecurity, which affects cognitive de-velopment (Johnson and Markowitz, 2018). Lower-income parents maywork multiple jobs and have less time for quality interactions with theirchildren. Finally, parents may not be able to financially invest in cog-nitively stimulating learning materials and trips (Bradley and Corwyn,2002). It may be that these aspects of SES (e.g., chronic stress) are moreaffected by income and less closely associated with parent education,and could in part help explain our result linking household income andchild neural processing. For instance, even if parents are less educated,if they have a higher income, they likely would not experience thestressors and risks discussed above.While a portion of our sample was economically strained, given the

high cost of living where this study was conducted, the majority ofparents were college educated. Thus, we may not have had the varia-bility necessary to detect effects of parent education. Future researchincluding a more diversely educated sample is critical to assessing therole of parent education for the P3b. It is possible with more educationvariability we would see differences in the P3b. Indeed, income and

Fig. 2. The parietal electrodes used in the current study.

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parent education were linked to different aspects of brain structure(Noble et al., 2015). Parent education has also related to ERPs of au-ditory selective attention (Stevens et al., 2009). Future research in-cluding a sample that spans income and parent education continuums,

as well as assessing ERPs of different aspects of EF, is needed to betterunderstand how these SES indices matter for EF neural processing.A strength of our study is that we assessed SES continuously. We

demonstrate that differences in neural processing are not only present

Fig. 3. a. Grand-averaged waveform of go and no-go trials forthe entire sample in the parietal region. The P3b was calcu-lated as the mean amplitude from 400 to 700ms (seen with thedashed lines). Four children had adjusted time windows toensure the P3b was represented. For one child, their timewindow was 300–600ms and three children had time win-dows of 350–650ms. Time 0ms indicates stimulus onset. b.Grand-averaged ERP waveforms for low and high ITN groupsfor go trials. The P3b was calculated as the mean amplitudefrom 400 to 700ms (seen with the dashed lines). Time 0msindicates stimulus onset. Four children had adjusted timewindows to ensure the P3b was represented. For one child,their time window was 300–600ms and three children hadtime windows of 350–650ms. Note. A median split was used tovisually depict the relation between ITN and the P3b on gotrials. ITN was analyzed continuously. c. Grand-averaged ERPwaveforms for low and high ITN groups for no-go trials. TheP3b was calculated as the mean amplitude from 400 to 700ms(seen with the dashed lines). Time 0ms indicates stimulusonset. Four children had adjusted time windows to ensure theP3b was represented. For one child, their time window was300–600ms and three children had time windows of350–650ms. Note. A median split was used to visually depictthe relation between ITN and the P3b on no-go trials. ITN wasanalyzed continuously.

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when comparing extreme SES groups, but are evident across the incomespectrum. This is consistent with a study showing variation in brainstructure on an SES continuum (Noble et al., 2015) and also with ameta-analysis, which showed child behavioral EF varied across the SESspectrum (Lawson et al., 2017). In addition, we build on past studieswhich have used dichotomous groups to demonstrate SES differences inneural processing (D’Angiulli et al., 2008; Kishiyama et al., 2008;Stevens et al., 2009). However, a limitation is that we did not pre-register our hypotheses, specifically that we expected higher levels ofincome and education to be associated with higher P3b amplitudes.We also assessed how SES related to behavioral performance on the

go/no-go task.ITN and parent education did not relate to any behavioral measure.

Thus, within our subsample that had useable ERP data, SES differencesin P3b amplitudes were more pronounced than behavioral differences.However, past research has found SES differences on behavioral per-formance of the go/no-go task (Noble et al., 2007, 2005). Future re-search including a larger sample in a longitudinal study is critical tomore thoroughly examine the role of SES for both the P3b and beha-vioral performance.Our data suggested that the go/no-go task did elicit the expected

P3b amplitudes. As we anticipated and consistent with the literature(Abdul Rahman et al., 2017; Davis et al., 2003; Falkenstein et al.,1999), P3b amplitudes were larger on no-go trials compared to go trials.In addition, there was a link between behavioral performance and theP3b such that children who had higher accuracy on go trials had largerP3b amplitudes on go trials. This suggests that the P3b does indeedindex neural processing of sustaining or maintaining attention.While to our knowledge, no studies have assessed how indices of

SES relate to P3b amplitudes on a go/no-go task, we did not expect anSES by trial type interaction as behavioral differences by SES are ty-pically seen for both inhibitory control and attention (Dilworth-Bartet al., 2007; Mezzacappa, 2004; Noble et al., 2005). In our study, SESdid not differentially relate to the neural processing of sustained at-tention (i.e., go trials) and to inhibition (i.e., no-go trials). Instead, wesaw a global effect such that children from families with higher incomeshad larger P3b amplitudes on both of these trial types. This is consistentwith SES-linked behavioral differences in both inhibitory control andattention. It is possible that the variation in the P3b by income levelindicates the extent to which children recruit neural systems. Researchsuggests that P3b amplitudes increase with age through adolescence(see for review, Downes et al., 2017). Thus it is possible that childrenfrom higher income families are showing more mature neural proces-sing and therefore show increased P3b amplitudes on both go and no-go

trials. A longitudinal study assessed ERPs in low and higher SES chil-dren at ages 4 and 5 using an auditory selective attention task (Wrayet al., 2017). Results showed that low SES children at age 5 showedsimilar ERP patterns to the high SES children at age 4, suggesting thatlow SES children were delayed relative to their high SES peers. Futureresearch exploring how income level relates to the P3b over time isneeded to further tease apart this possibility.Assessing the neural correlates of inhibition and attention is im-

portant for its potential to help explain SES differences in behavioral EFand academic success. An important future direction is to assess long-itudinal relations between SES, ERPs, and later outcomes to move to-wards understanding how neural processing may be a mechanism forunderstanding how SES impacts later outcomes. Further, the currentstudy only included one task that tapped certain aspects of EF. Futureresearch including tasks that index additional EF skills, such as workingmemory and cognitive flexibility, are critical for understanding the roleof SES for children’s neural processing. Additionally, while this studycannot speak to causal relations between SES and neural processing, itcontributes to characterizing SES differences in cognitive functioning.This can help inform the development of experimental designs to testfor causal relations, with eventual implications of designing target in-terventions (Hackman and Farah, 2009). Moreover, our findings ex-pand upon the broader literature on effects of SES for the developingbrain by demonstrating that SES relates to the neural processing ofinhibition and attention processes. Together, this body of research un-derscores how early in life the brain is sensitive to socioeconomiccontext. This has serious implications for policy efforts to address thesocioeconomic gap early before SES differences are entrenched.Given that ERPs may be a tool to identify children at risk, in-

formation on how specific indices of SES are shaping ERPs can betterinform policy and intervention efforts. We demonstrate that by pre-school age, children are already showing differences in neural proces-sing, with implications for later inhibition and attention processes. It isnoteworthy that income was implicated for neural processing, despitethe high level of education in our sample. This fits in with a recentmovement for boosting family incomes with the hopes of improvingchild outcomes (Duncan et al., 2014).The nature of the cognitive processes indexed by the P3b is complex

and researchers offer different interpretations of what the P3b practi-cally means. In addition to being interpreted as an index of attention,inhibition and controlled processing (Polich, 2007), the P3b has alsobeen proposed to index context updating and working memory pro-cesses (Donchin, 1981). In a way, working memory is inherently in-volved in all tasks, given that to perform a task correctly, one must hold

Table 4Correlations of Study Variables.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

1.ITN –2.EDU .47*** –3.P3b go amp .26* .17 –4.P3b no-go amp .22† .12 .86*** –5.ACC go amp .12 .20† .27* .18 –6.ACC no-go amp .06 −.03 .00 −.13 −.10 –7.RT go −.01 .07 −.12 -.09 -.54*** .48*** –8.Lang-

uage.33** .19 .12 .11 −.01 .09 .09 –

9.Non-verbal IQ

.19 .04 −.03 −.02 −.06 .27* .03 .44*** –

10.Age .02 .02 .11 .07 .41*** −.08 0.24† −.22† −.14 –11.Gender -.04 -.03 .15 −.03 −.16 .19 .03 −.09 −.18 −.23† –

Note. ITN= income-to-needs ratio; EDU=parent education; amp=amplitude; ACC=accuracy; RT= reaction time.† p

the instructions/rules in working memory. Indeed, larger P3b ampli-tudes have been related to better performance on the backward digitspan, an assessment of working memory (Brydges et al., 2014). Re-gardless of the exact interpretation of the P3b, our study is a first step indemonstrating that neural processing of inhibition and attention pro-cesses varies by socioeconomic context. Future research is needed tomore precisely characterize the nature of this relation.

5. Conclusion

The relation of lower SES and poorer behavioral EF is well estab-lished. We demonstrate that by kindergarten entry, there are alreadySES-linked differences in the neural processing of inhibitory control andsustained attention. Specifically, higher income related to larger P3bamplitudes in 4.5–5.5 year olds, despite the highly educated nature ofthe sample. This study adds to the growing literature on effects of SESon the developing brain and demonstrates that household income isimportant for neural processes of attention and inhibition.

Declaration of Competing Interest

The authors have no conflicts of interest to report.

Funding

This research was supported by the American PsychologicalAssociation of Graduate Students Basic Psychological Research Grantand the Boston University Clara Mayo Memorial Fellowship to AshleySt. John.

Acknowledgements

We thank Basak Oztahtaci and the Brain and Early Experiences Labfor assistance in data collection. We are extremely grateful to thechildren and families who participated, without whom this work wouldnot be possible.

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Socioeconomic status and neural processing of a go/no-go task in preschoolers: An assessment of the P3bIntroductionEvent-related potentials and the P3bIncome, parent education, and neural processingCurrent study

MethodsParticipantsGeneral procedureMeasuresGo/No-go

Household incomeParent educationTested covariatesLanguageNonverbal IQ

Electrophysiological recording and analysisAnalysis plan

ResultsPreliminary analysesRelations of ITN and parent education to P3b amplitudes

DiscussionConclusionDeclaration of Competing InterestFundingAcknowledgementsReferences